Deinterlacing Methods Research Articles

De-interlacing technique based on total variation with spatial-temporal smoothness constraint

This paper introduces a new method of converting interlaced video to a progressively scanned video and image. The new method is derived from Bayesian framework with the spatial-temporal smoothness constraint and the MAP is done by minimizing the energy functional. The half-quadratic regularization method is used to solve the corresponding partial differential equations (PDEs). This approach gives the improved results over the conventional de-interlacing methods. Two criteria are proposed in the paper, and they can be used to evaluate the performance of the de-interlacing algorithms.

Fuzzy Rule and Bayesian Network Based Line Interpolation for Video Deinterlacing

Detecting edge directions and estimating the exact value of a missing line are currently active research areas in deinterlacing processing. This paper proposes a spatial domain fuzzy rule that is based on an interpolation algorithm, which is suitable to the region with high motion or scene change. The algorithm utilizes fuzzy theory to find the most accurate edge direction with which to interpolate missing pixels. The proposed fuzzy direction oriented interpolator operates by identifying small pixel variations in seven orientations (0°, 45°, -45°, 63°, -63°, 72°, and -72°), while using rules to infer the edge direction. The Bayesian network model selects the most suitable deinterlacing method among three deinterlacing methods and it successively builds approximations of the deinter-laced sequence, by evaluating three methods in each condition. Detection and interpolation results are presented. Experimental results show that the proposed algorithm provides a significant improvement over other existing deinterlacing methods. The proposed algorithm is not only for speed, but also effective for reducing deinterlacing artifacts.

Motion adaptive deinterlacing of video data with texture detection

In this paper, we propose a motion adaptive deinterlacing method with texture detection for scan-rate conversion of video data. The basic idea of this method is to classify the missing pixel to four different regions including motion smooth region, motion texture region, static smooth region, and static texture region based on the results of motion detection and texture detection, then four different interpolation methods are used to produce the missing pixel. Extensive simulations conducted for video sequences indicate that the performance of the proposed method is superior to the previous methods based on the direct merging method, bilinear interpolation, ELA, and vertical temporal median method. Moreover, our proposed method requires lower computational complexity and provides better object and subject performance

Designing takagi-sugeno fuzzy model-based motion adaptive deinterlacing system

This paper proposes a novel interpolation algorithm based on the Takagi-Sugeno (TS) fuzzy model, which uses fuzzy direction-oriented interpolation (FDOI). The proposed algorithm can choose the most suitable deinterlacing methods for interpolation of a missing pixel by using an area classifier that classifies the content of a frame into three different categories. The proposed FDOI utilizes fuzzy theory to find the most accurate edge direction in which to interpolate missing pixels. The shape of the membership functions used in fuzzy logic are designed adaptively according to the parameters. Detection and interpolation results are presented. The results of computer simulations show that the proposed method outperforms a number of methods in the literature

Deinterlacing with selective motion compensation

We propose a new deinterlacing algorithm with selective motion compensation. It has been reported that deinterlacing methods using motion compensation produce significantly improved results, although they tend to yield undesired results in fast moving areas. This is due to weak correlations between the previous and current frames. The proposed algorithm solves this problem by selectively applying motion-compensated deinterlacing. We first apply intrafield interpolation in the spatial domain, and then selectively apply motion compensations according to the type of motion vectors. Experimental results show that the proposed method produces noticeably improved performance compared to existing motion-compensated deinterlacing methods.

Stereoscopic demultiplexing of field-sequential video

Much research has been devoted to investigating the generation of stereoscopic video based on catadioptric stereo systems. These systems, which utilize a proper combination of mirrors and lens, can capture stereoscopic video using only one camera. The left and right images are usually multiplexed to generate an interlaced video where each image corresponds to one field. This field-sequential format unavoidably reduces vertical resolution of the image pair by half. We proposed a notion called stereoscopic demultiplexing (SD) to recover the original resolution of the image pairs from the field-sequential video. To increase the sharpness of the image and avoid jagged artifacts, interpolation along the edge direction is emphasized. Because the SD problem is similar to deinterlacing, several methods previously used in deinterlacing were evaluated, and a new edge adaptive interpolation (EAI) approach was proposed. This new method can estimate edge direction with higher accuracy and obtain better image quality. Several experimental results show that the proposed algorithm outperforms the other popular deinterlacing methods both visually and quantitatively. Since resolution of the display technology is higher and better than that of current catadioptric stereo systems, we believe that further improvement in the resolution of the acquisition device by software is necessary to make 3-D applications more economical and popular.

De-interlacing algorithms based on motion compensation

Motion estimation (ME) and motion compensation (MC) based deinterlacing has been become the mainstream technology of industry. The existing ME/MC algorithms are mostly based on block matching (BM) strategy, in which various BM algorithms are proposed with different tradeoff between the matching accuracy and the CPU time. Aiming at the dilemma between the performance and the efficiency of the available deinterlacing algorithms, two adaptive ME/MC algorithms are presented in this paper. Compared with the full search BM algorithm, the proposed algorithms greatly reduce the computational amount with keeping the performance approximately. However, the inherent drawbacks of the BM limit the farther improvement of the deinterlacing performance. For this purpose, a new deinterlacing algorithm based on motion object is developed, in which it is natural motion object rather than contrived block that is taken as the basic cell for ME/MC. In the new algorithm, a more reliable technique is adopted for accurate motion region detection, and immune clonal selection algorithm is employed to accelerate the matched object searching. Meanwhile, many advanced deinterlacing methods, such as motion compensation, median filtering, Weave and Bob, are integrated into the new algorithm, so it is more adaptive to the various video sequences. The experimental results with several available test databases illustrate that the new algorithm based on motion object is better than those based on motion block on the whole.

A Picture Format Converter Using Motion Compensated De-Interlacing

Picture format conversion is an important technology for multiformat broadcast. Many interlaced DTV transmissions are likely to be conversions from other interlaced acquisition formats and the main topic of this paper will be conversion from one interlaced picture to another. Practical realization of this conversion is accomplished by dividing the processing into two parts, de-interlacing and line resampling. In this structure, suitable de-interlacing can be applied using motion compensation. Further, a new de-interlacing method is described to solve problems introduced by motion compensation.

A method of de-interlacing with motion compensated interpolation

De-interlacing is a picture format conversion from an interlaced picture to a progressive picture. Multi-format digital broadcast and progressive display require de-interlacing technology. For this purpose, a motion adaptive method is used widely. In the motion adaptive method, the picture quality is not high enough in the motion area. Motion compensation may be effective for the improvement of picture quality. However, it sometimes causes visible distortion. We propose a new method to solve the problems of motion compensation. It consists of small blocks with large motion estimation, adaptive interpolation control by intra/inter differences and smooth motion switching. We measured the performance of the de-interlacing method objectively. It is found that the SNR of the proposed method becomes 4 to 7 dB better than the non-compensation method.

A motion-adaptive de-interlacing method

The authors describe an improved motion-adaptive de-interlacing method that incorporates the missing lines of interlaced fields from pixels of the same field or adjacent fields after motion compensation. Some improvements were obtained by suppressing the effect of false motion caused by interpolation error in the searched field, and by interpolation filtering based on the characteristics of moving objects. Experimental data show that the proposed method resulted in smaller interpolation error than conventional de-interlacing methods.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>